# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.

# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.


# All contributions by NVIDIA CORPORATION:
# Copyright (c) 2021, NVIDIA CORPORATION.  All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto.  Any use, reproduction, disclosure or
# distribution of this software and related documentation without an express
# license agreement from NVIDIA CORPORATION is strictly prohibited.

import os
import time
import copy
import json
import pickle
import psutil
import PIL.Image
import numpy as np
import shutil

import torch
import dnnlib
from torch_utils import misc
from torch_utils import training_stats
from torch_utils.ops import conv2d_gradfix
from torch_utils.ops import grid_sample_gradfix

from torchvision import transforms

import legacy
from metrics import metric_main
import torch.distributed as dist

# ----------------------------------------------------------------------------


def setup_snapshot_image_grid(training_set, random_seed=0):
    rnd = np.random.RandomState(random_seed)
    gw = np.clip(7680 // training_set.image_shape[2], 7, 32)
    gh = np.clip(4320 // training_set.image_shape[1], 4, 32)

    # No labels => show random subset of training samples.
    if not training_set.has_labels:
        all_indices = list(range(len(training_set)))
        rnd.shuffle(all_indices)
        grid_indices = [all_indices[i % len(all_indices)] for i in range(gw * gh)]

    else:
        # Group training samples by label.
        label_groups = dict()  # label => [idx, ...]
        for idx in range(len(training_set)):
            label = tuple(training_set.get_details(idx).raw_label.flat[::-1])
            if label not in label_groups:
                label_groups[label] = []
            label_groups[label].append(idx)

        # Reorder.
        label_order = sorted(label_groups.keys())
        for label in label_order:
            rnd.shuffle(label_groups[label])

        # Organize into grid.
        grid_indices = []
        for y in range(gh):
            label = label_order[y % len(label_order)]
            indices = label_groups[label]
            grid_indices += [indices[x % len(indices)] for x in range(gw)]
            label_groups[label] = [
                indices[(i + gw) % len(indices)] for i in range(len(indices))
            ]

    # Load data.
    images, labels = zip(*[training_set[i] for i in grid_indices])
    return (gw, gh), np.stack(images), np.stack(labels)


# ----------------------------------------------------------------------------


def save_image_grid(img, fname, drange, grid_size):
    lo, hi = drange
    img = np.asarray(img, dtype=np.float32)
    img = (img - lo) * (255 / (hi - lo))
    img = np.rint(img).clip(0, 255).astype(np.uint8)

    gw, gh = grid_size
    _N, C, H, W = img.shape
    img = img.reshape(gh, gw, C, H, W)
    img = img.transpose(0, 3, 1, 4, 2)
    img = img.reshape(gh * H, gw * W, C)

    assert C in [1, 3]
    if C == 1:
        PIL.Image.fromarray(img[:, :, 0], "L").save(fname)
    if C == 3:
        PIL.Image.fromarray(img, "RGB").save(fname)


# ----------------------------------------------------------------------------


def training_loop(
    exp_name="default_name",
    run_dir=".",  # Output directory.
    temp_dir=".",  # Temporary directory.
    training_set_kwargs={},  # Options for training set.
    data_loader_kwargs={},  # Options for torch.utils.data.DataLoader.
    G_kwargs={},  # Options for generator network.
    D_kwargs={},  # Options for discriminator network.
    G_opt_kwargs={},  # Options for generator optimizer.
    D_opt_kwargs={},  # Options for discriminator optimizer.
    augment_kwargs=None,  # Options for augmentation pipeline. None = disable.
    loss_kwargs={},  # Options for loss function.
    class_cond=False,  # Condition on class labels.
    instance_cond=False,  # Condition on instance features.
    metrics=[],  # Metrics to evaluate during training.
    random_seed=0,  # Global random seed.
    num_gpus=1,  # Number of GPUs participating in the training.
    slurm=False,  # Launching the experiment in SLURM.
    rank=0,  # Rank of the current process in [0, num_gpus[.
    local_rank=0,  # Local rank of the current process inside each node [0, num_gpus_per_node]
    batch_size=4,  # Total batch size for one training iteration. Can be larger than batch_gpu * num_gpus.
    batch_gpu=4,  # Number of samples processed at a time by one GPU.
    ema_kimg=10,  # Half-life of the exponential moving average (EMA) of generator weights.
    ema_rampup=None,  # EMA ramp-up coefficient.
    G_reg_interval=4,  # How often to perform regularization for G? None = disable lazy regularization.
    D_reg_interval=16,  # How often to perform regularization for D? None = disable lazy regularization.
    augment_p=0,  # Initial value of augmentation probability.
    ada_target=None,  # ADA target value. None = fixed p.
    ada_interval=4,  # How often to perform ADA adjustment?
    ada_kimg=500,  # ADA adjustment speed, measured in how many kimg it takes for p to increase/decrease by one unit.
    total_kimg=25000,  # Total length of the training, measured in thousands of real images.
    kimg_per_tick=4,  # Progress snapshot interval.
    image_snapshot_ticks=50,  # How often to save image snapshots? None = disable.
    network_snapshot_ticks=50,  # How often to save network snapshots? None = disable.
    es_patience=100000000,  # Early stopping patience expressed in number of images seen.
    resume_pkl=None,  # Network pickle to resume training from.
    cudnn_benchmark=True,  # Enable torch.backends.cudnn.benchmark?
    allow_tf32=False,  # Enable torch.backends.cuda.matmul.allow_tf32 and torch.backends.cudnn.allow_tf32?
    abort_fn=None,  # Callback function for determining whether to abort training. Must return consistent results across ranks.
    progress_fn=None,  # Callback function for updating training progress. Called for all ranks.
):
    # Initialize.
    start_time = time.time()

    device = "cuda:{}".format(local_rank)
    torch.cuda.set_device(device)
    # device = torch.device('cuda', rank)
    np.random.seed(random_seed * num_gpus + rank)
    torch.manual_seed(random_seed * num_gpus + rank)
    torch.backends.cudnn.benchmark = cudnn_benchmark  # Improves training speed.
    torch.backends.cuda.matmul.allow_tf32 = (
        allow_tf32
    )  # Allow PyTorch to internally use tf32 for matmul
    torch.backends.cudnn.allow_tf32 = (
        allow_tf32
    )  # Allow PyTorch to internally use tf32 for convolutions
    conv2d_gradfix.enabled = True  # Improves training speed.
    grid_sample_gradfix.enabled = True  # Avoids errors with the augmentation pipe.

    if slurm:
        img_filename = os.path.basename(training_set_kwargs.root)
        tmp_file_img = os.path.join(temp_dir, img_filename)
        if instance_cond:
            feats_filename = os.path.basename(training_set_kwargs.root_feats)
            tmp_file_feats = os.path.join(temp_dir, feats_filename)
        if local_rank == 0:
            print("start copying data locally")
            if not os.path.exists(tmp_file_img):
                shutil.copy2(training_set_kwargs.root, tmp_file_img)
            if instance_cond and not os.path.exists(tmp_file_feats):
                shutil.copy2(training_set_kwargs.root_feats, tmp_file_feats)
            print("finished copying data locally")
        dist.barrier()
        training_set_kwargs.root = tmp_file_img
        if instance_cond:
            training_set_kwargs.root_feats = tmp_file_feats
        print("Final path dataset ", training_set_kwargs.root)
        if instance_cond:
            print("Final path dataset (feats)", training_set_kwargs.root_feats)

    # Load training set.
    if rank == 0:
        print("Loading training set...")
    if training_set_kwargs.xflip:
        transform = transforms.RandomHorizontalFlip()
    else:
        transform = None
    training_set = dnnlib.util.construct_class_by_name(
        **{**training_set_kwargs, "transform": transform}
    )  # subclass of training.dataset.Dataset
    training_set_sampler = misc.InfiniteSampler(
        dataset=training_set, rank=rank, num_replicas=num_gpus, seed=random_seed
    )
    training_set_iterator = iter(
        torch.utils.data.DataLoader(
            dataset=training_set,
            sampler=training_set_sampler,
            batch_size=batch_size // num_gpus,
            **data_loader_kwargs,
        )
    )
    if rank == 0:
        print()
        print("Num images: ", len(training_set))
        print("Image shape:", training_set.resolution)
        print("Label shape:", training_set.label_dim)
        print("Features shape:", training_set.feature_dim)
        print()

    # Construct networks.
    if rank == 0:
        print("Constructing networks...")
    common_kwargs = dict(
        c_dim=training_set.label_dim if class_cond else 0,
        h_dim=training_set.feature_dim if instance_cond else 0,
        img_resolution=training_set.resolution,
        img_channels=3,
    )
    G = (
        dnnlib.util.construct_class_by_name(**G_kwargs, **common_kwargs)
        .train()
        .requires_grad_(False)
        .to(device)
    )  # subclass of torch.nn.Module
    D = (
        dnnlib.util.construct_class_by_name(**D_kwargs, **common_kwargs)
        .train()
        .requires_grad_(False)
        .to(device)
    )  # subclass of torch.nn.Module
    G_ema = copy.deepcopy(G).eval()

    snapshot_pkl_last = os.path.join(run_dir, "last_net")
    # Resume from existing pickle.
    if num_gpus > 1:
        dist.barrier()
    if (resume_pkl is not None) and (rank == 0):
        print(f'Resuming from "{resume_pkl}"')
        with dnnlib.util.open_url(resume_pkl) as f:
            resume_data = legacy.load_network_pkl(f)
        for name, module in [("G", G), ("D", D), ("G_ema", G_ema)]:
            misc.copy_params_and_buffers(resume_data[name], module, require_all=False)
        print("Successfully loaded G,D,G_ema from specific pkl checkpoint")
    else:
        try:
            print(f'Resuming from "{snapshot_pkl_last}".pkl')
            with dnnlib.util.open_url(snapshot_pkl_last + ".pkl") as f:
                resume_data = legacy.load_network_pkl(f)
            for name, module in [("G", G), ("D", D), ("G_ema", G_ema)]:
                misc.copy_params_and_buffers(
                    resume_data[name], module, require_all=False
                )
            print("Successfully loaded G,D,G_ema from last checkpoint")
        except:
            print("Starting training from scratch")

    # Print network summary tables.
    if rank == 0:
        z = torch.empty([batch_gpu, G.z_dim], device=device)
        c = torch.empty([batch_gpu, G.c_dim], device=device)
        h = torch.empty([batch_gpu, G.h_dim], device=device)
        img = misc.print_module_summary(G, [z, c, h])
        misc.print_module_summary(D, [img, c, h])

    # Setup augmentation.
    if rank == 0:
        print("Setting up augmentation...")
    augment_pipe = None
    ada_stats = None
    if (augment_kwargs is not None) and (augment_p > 0 or ada_target is not None):
        augment_pipe = (
            dnnlib.util.construct_class_by_name(**augment_kwargs)
            .train()
            .requires_grad_(False)
            .to(device)
        )  # subclass of torch.nn.Module
        augment_pipe.p.copy_(torch.as_tensor(augment_p))
        if ada_target is not None:
            ada_stats = training_stats.Collector(regex="Loss/signs/real")

    # Distribute across GPUs.
    if rank == 0:
        print(f"Distributing across {num_gpus} GPUs...")
    ddp_modules = dict()
    for name, module in [
        ("G_mapping", G.mapping),
        ("G_synthesis", G.synthesis),
        ("D", D),
        (None, G_ema),
        ("augment_pipe", augment_pipe),
    ]:
        if (
            (num_gpus > 1)
            and (module is not None)
            and len(list(module.parameters())) != 0
        ):
            module.requires_grad_(True)
            module = torch.nn.parallel.DistributedDataParallel(
                module, device_ids=[device], broadcast_buffers=False
            )
            module.requires_grad_(False)
        if name is not None:
            ddp_modules[name] = module

    # Setup training phases.
    if rank == 0:
        print("Setting up training phases...")
    loss = dnnlib.util.construct_class_by_name(
        device=device, **ddp_modules, **loss_kwargs
    )  # subclass of training.loss.Loss
    phases = []
    for name, module, opt_kwargs, reg_interval in [
        ("G", G, G_opt_kwargs, G_reg_interval),
        ("D", D, D_opt_kwargs, D_reg_interval),
    ]:
        if reg_interval is None:
            opt = dnnlib.util.construct_class_by_name(
                params=module.parameters(), **opt_kwargs
            )  # subclass of torch.optim.Optimizer
            phases += [
                dnnlib.EasyDict(name=name + "both", module=module, opt=opt, interval=1)
            ]
        else:  # Lazy regularization.
            mb_ratio = reg_interval / (reg_interval + 1)
            opt_kwargs = dnnlib.EasyDict(opt_kwargs)
            opt_kwargs.lr = opt_kwargs.lr * mb_ratio
            opt_kwargs.betas = [beta ** mb_ratio for beta in opt_kwargs.betas]
            opt = dnnlib.util.construct_class_by_name(
                module.parameters(), **opt_kwargs
            )  # subclass of torch.optim.Optimizer
            phases += [
                dnnlib.EasyDict(name=name + "main", module=module, opt=opt, interval=1)
            ]
            phases += [
                dnnlib.EasyDict(
                    name=name + "reg", module=module, opt=opt, interval=reg_interval
                )
            ]
    for phase in phases:
        phase.start_event = None
        phase.end_event = None
        if rank == 0:
            phase.start_event = torch.cuda.Event(enable_timing=True)
            phase.end_event = torch.cuda.Event(enable_timing=True)

    # Resume from existing checkpoint.
    if num_gpus > 1:
        dist.barrier()
    print("Resuming optimizers ")
    try:
        for phase in phases:
            phase["opt"].load_state_dict(
                torch.load(
                    snapshot_pkl_last + phase["name"] + "_opt.pth", map_location=device
                )
            )
        print("All optimizers loaded from checkpoint! ")
    except:
        print("Could not load checkpoint! ", snapshot_pkl_last)
        print("Starting training from scratch")

    # Export sample images.
    grid_size = None
    grid_z = None
    grid_c = None
    # if rank == 0 and False:
    #     print('Exporting sample images...')
    #     grid_size, images, labels = setup_snapshot_image_grid(training_set=training_set)
    #     save_image_grid(images, os.path.join(run_dir, 'reals.png'), drange=[0,255], grid_size=grid_size)
    #     grid_z = torch.randn([labels.shape[0], G.z_dim], device=device).split(batch_gpu)
    #     grid_c = torch.from_numpy(labels).to(device).split(batch_gpu)
    #     images = torch.cat([G_ema(z=z, c=c, noise_mode='const').cpu() for z, c in zip(grid_z, grid_c)]).numpy()
    #     save_image_grid(images, os.path.join(run_dir, 'fakes_init.png'), drange=[-1,1], grid_size=grid_size)

    # Initialize logs.
    if rank == 0:
        print("Initializing logs...")
    stats_collector = training_stats.Collector(regex=".*")
    stats_metrics = dict()
    stats_jsonl = None
    stats_tfevents = None
    if rank == 0:
        stats_jsonl = open(os.path.join(run_dir, "stats.jsonl"), "wt")
        try:
            import torch.utils.tensorboard as tensorboard

            stats_tfevents = tensorboard.SummaryWriter(run_dir)
        except ImportError as err:
            print("Skipping tfevents export:", err)

    # Train.
    if rank == 0:
        print(f"Training for {total_kimg} kimg...")
        print()
    cur_nimg = 0
    cur_tick = 0
    try:
        (cur_tick, cur_nimg) = np.load(
            snapshot_pkl_last + "last_itr.npy", allow_pickle=True
        )
        print("Loading last tick and nimg ", cur_tick, cur_nimg)
    except:
        print("No last iter to load, starting from scratch")

    best_fid = 1000000
    best_fid_nimg = 0
    try:
        (_, best_fid_nimg, best_fid) = np.load(
            os.path.join(run_dir, "best_fid_itr.npy"), allow_pickle=True
        )
        print(f"Loading best fid itr {best_fid_nimg}, value of fid is {best_fid}")
    except:
        print("No last iter to load for best fid, starting from scratch")

    tick_start_nimg = cur_nimg
    tick_start_time = time.time()
    maintenance_time = tick_start_time - start_time
    batch_idx = 0
    if progress_fn is not None:
        progress_fn(cur_nimg, total_kimg)
    while True:
        # Fetch training data.
        with torch.autograd.profiler.record_function("data_fetch"):
            batch = next(training_set_iterator)
            if instance_cond and class_cond:
                phase_real_img, phase_real_c, phase_real_h, _ = batch
            elif instance_cond:
                phase_real_img, phase_real_h, _ = batch
                phase_real_c = torch.empty([batch_gpu, G.c_dim], device=device)
            elif class_cond:
                phase_real_img, phase_real_c = batch
                phase_real_h = torch.empty([batch_gpu, G.h_dim], device=device)
            else:
                phase_real_img = batch
                phase_real_c = torch.empty([batch_gpu, G.c_dim], device=device)
                phase_real_h = torch.empty([batch_gpu, G.h_dim], device=device)

            phase_real_img = (
                phase_real_img.to(device).to(torch.float32) / 127.5 - 1
            ).split(batch_gpu)
            all_gen_c = [
                training_set.get_label(np.random.randint(len(training_set)))
                for _ in range(len(phases) * batch_size)
            ]  # take random labels
            all_gen_h = [
                training_set.get_instance_features(np.random.randint(len(training_set)))
                for _ in range(len(phases) * batch_size)
            ]  # take random instance features

            if class_cond:
                phase_real_c = phase_real_c.to(device).split(batch_gpu)
            if instance_cond:
                phase_real_h = phase_real_h.to(device).split(batch_gpu)
            all_gen_z = torch.randn([len(phases) * batch_size, G.z_dim], device=device)
            all_gen_z = [
                phase_gen_z.split(batch_gpu)
                for phase_gen_z in all_gen_z.split(batch_size)
            ]
            all_gen_c = torch.from_numpy(np.stack(all_gen_c)).pin_memory().to(device)
            all_gen_c = [
                phase_gen_c.split(batch_gpu)
                for phase_gen_c in all_gen_c.split(batch_size)
            ]
            all_gen_h = torch.from_numpy(np.stack(all_gen_h)).pin_memory().to(device)
            all_gen_h = [
                phase_gen_h.split(batch_gpu)
                for phase_gen_h in all_gen_h.split(batch_size)
            ]
        # Execute training phases.
        for phase, phase_gen_z, phase_gen_c, phase_gen_h in zip(
            phases, all_gen_z, all_gen_c, all_gen_h
        ):
            if batch_idx % phase.interval != 0:
                continue

            # Initialize gradient accumulation.
            if phase.start_event is not None:
                phase.start_event.record(torch.cuda.current_stream(device))
            phase.opt.zero_grad(set_to_none=True)
            phase.module.requires_grad_(True)

            # Accumulate gradients over multiple rounds.
            for round_idx, (real_img, real_c, real_h, gen_z, gen_c, gen_h) in enumerate(
                zip(
                    phase_real_img,
                    phase_real_c,
                    phase_real_h,
                    phase_gen_z,
                    phase_gen_c,
                    phase_gen_h,
                )
            ):
                sync = round_idx == batch_size // (batch_gpu * num_gpus) - 1
                gain = phase.interval
                loss.accumulate_gradients(
                    phase=phase.name,
                    real_img=real_img,
                    real_c=real_c,
                    real_h=real_h,
                    gen_z=gen_z,
                    gen_c=gen_c,
                    gen_h=gen_h,
                    sync=sync,
                    gain=gain,
                )

            # Update weights.
            phase.module.requires_grad_(False)
            with torch.autograd.profiler.record_function(phase.name + "_opt"):
                for param in phase.module.parameters():
                    if param.grad is not None:
                        misc.nan_to_num(
                            param.grad, nan=0, posinf=1e5, neginf=-1e5, out=param.grad
                        )
                phase.opt.step()
            if phase.end_event is not None:
                phase.end_event.record(torch.cuda.current_stream(device))

        # Update G_ema.
        with torch.autograd.profiler.record_function("Gema"):
            ema_nimg = ema_kimg * 1000
            if ema_rampup is not None:
                ema_nimg = min(ema_nimg, cur_nimg * ema_rampup)
            ema_beta = 0.5 ** (batch_size / max(ema_nimg, 1e-8))
            for p_ema, p in zip(G_ema.parameters(), G.parameters()):
                p_ema.copy_(p.lerp(p_ema, ema_beta))
            for b_ema, b in zip(G_ema.buffers(), G.buffers()):
                b_ema.copy_(b)

        # Update state.
        cur_nimg += batch_size
        batch_idx += 1

        # Execute ADA heuristic.
        if (ada_stats is not None) and (batch_idx % ada_interval == 0):
            ada_stats.update()
            adjust = (
                np.sign(ada_stats["Loss/signs/real"] - ada_target)
                * (batch_size * ada_interval)
                / (ada_kimg * 1000)
            )
            augment_pipe.p.copy_(
                (augment_pipe.p + adjust).max(misc.constant(0, device=device))
            )

        # Perform maintenance tasks once per tick.
        done = cur_nimg >= total_kimg * 1000
        if (
            (not done)
            and (cur_tick != 0)
            and (cur_nimg < tick_start_nimg + kimg_per_tick * 1000)
        ):
            continue

        # Print status line, accumulating the same information in stats_collector.
        tick_end_time = time.time()
        fields = []
        fields += [f"tick {training_stats.report0('Progress/tick', cur_tick):<5d}"]
        fields += [
            f"kimg {training_stats.report0('Progress/kimg', cur_nimg / 1e3):<8.1f}"
        ]
        fields += [
            f"time {dnnlib.util.format_time(training_stats.report0('Timing/total_sec', tick_end_time - start_time)):<12s}"
        ]
        fields += [
            f"sec/tick {training_stats.report0('Timing/sec_per_tick', tick_end_time - tick_start_time):<7.1f}"
        ]
        fields += [
            f"sec/kimg {training_stats.report0('Timing/sec_per_kimg', (tick_end_time - tick_start_time) / (cur_nimg - tick_start_nimg) * 1e3):<7.2f}"
        ]
        fields += [
            f"maintenance {training_stats.report0('Timing/maintenance_sec', maintenance_time):<6.1f}"
        ]
        fields += [
            f"cpumem {training_stats.report0('Resources/cpu_mem_gb', psutil.Process(os.getpid()).memory_info().rss / 2**30):<6.2f}"
        ]
        fields += [
            f"gpumem {training_stats.report0('Resources/peak_gpu_mem_gb', torch.cuda.max_memory_allocated(device) / 2**30):<6.2f}"
        ]
        torch.cuda.reset_peak_memory_stats()
        fields += [
            f"augment {training_stats.report0('Progress/augment', float(augment_pipe.p.cpu()) if augment_pipe is not None else 0):.3f}"
        ]
        training_stats.report0(
            "Timing/total_hours", (tick_end_time - start_time) / (60 * 60)
        )
        training_stats.report0(
            "Timing/total_days", (tick_end_time - start_time) / (24 * 60 * 60)
        )
        if rank == 0:
            print(" ".join(fields))

        # Check for abort.
        if (not done) and (abort_fn is not None) and abort_fn():
            done = True
            if rank == 0:
                print()
                print("Aborting...")

        # # Save image snapshot.
        # if (rank == 0) and (image_snapshot_ticks is not None) and (done or cur_tick % image_snapshot_ticks == 0):
        #     images = torch.cat([G_ema(z=z, c=c, noise_mode='const').cpu() for z, c in zip(grid_z, grid_c)]).numpy()
        #     save_image_grid(images, os.path.join(run_dir, f'fakes{cur_nimg//1000:06d}.png'), drange=[-1,1], grid_size=grid_size)

        # Save network snapshot.
        snapshot_pkl = None
        snapshot_data = None
        if (network_snapshot_ticks is not None) and (
            done or cur_tick % network_snapshot_ticks == 0
        ):
            snapshot_data = dict(training_set_kwargs=dict(training_set_kwargs))
            snapshot_pkl = os.path.join(
                run_dir, f"network-snapshot-{cur_nimg//1000:06d}.pkl"
            )
            for name, module in [
                ("G", G),
                ("D", D),
                ("G_ema", G_ema),
                ("augment_pipe", augment_pipe),
            ]:
                if module is not None:
                    # if not slurm and num_gpus > 1:
                    #     misc.check_ddp_consistency(module, ignore_regex=r'.*\.w_avg')
                    module = copy.deepcopy(module).eval().requires_grad_(False).cpu()
                snapshot_data[name] = module
                del module  # conserve memory
                # if rank == 0:
                #     with open(snapshot_pkl, 'wb') as f:
                #         pickle.dump(snapshot_data, f)

                # Save last checkpoint as well
                with open(snapshot_pkl_last + ".pkl", "wb") as f:
                    pickle.dump(snapshot_data, f)
                for phase in phases:
                    torch.save(
                        phase["opt"].state_dict(),
                        snapshot_pkl_last + phase["name"] + "_opt.pth",
                    )
                np.save(snapshot_pkl_last + "last_itr", (cur_tick, cur_nimg))

        # Evaluate metrics.
        if (snapshot_data is not None) and (len(metrics) > 0):
            if rank == 0:
                print("Evaluating metrics...")
            for metric in metrics:
                result_dict = metric_main.calc_metric(
                    metric=metric,
                    G=snapshot_data["G_ema"],
                    dataset_kwargs=training_set_kwargs,
                    num_gpus=num_gpus,
                    rank=rank,
                    device=device,
                )
                if rank == 0:
                    metric_main.report_metric(
                        result_dict, run_dir=run_dir, snapshot_pkl=snapshot_pkl
                    )
                stats_metrics.update(result_dict.results)

                if metric == "fid50k_full" and rank == 0:
                    cur_fid = result_dict["results"]["fid50k_full"]
                    if cur_fid < best_fid:
                        print("Saving network snapshot with best FID")
                        best_fid = cur_fid
                        best_fid_nimg = cur_nimg
                        snapshot_best_pkl = os.path.join(
                            run_dir, f"best-network-snapshot.pkl"
                        )
                        with open(snapshot_best_pkl, "wb") as f:
                            pickle.dump(snapshot_data, f)
                        np.save(
                            os.path.join(run_dir, "best_fid_itr"),
                            (cur_tick, cur_nimg, best_fid),
                        )
                    else:  # stopping criterion : if fid stops decreasing during es_patience nimg: stop training
                        if (cur_nimg - best_fid_nimg) > es_patience:
                            done = True
                            print("Stopping training due to early stopping.")
        del snapshot_data  # conserve memory

        # Collect statistics.
        for phase in phases:
            value = []
            if (phase.start_event is not None) and (phase.end_event is not None):
                phase.end_event.synchronize()
                value = phase.start_event.elapsed_time(phase.end_event)
            training_stats.report0("Timing/" + phase.name, value)
        stats_collector.update()
        stats_dict = stats_collector.as_dict()

        # Update logs.
        timestamp = time.time()
        if stats_jsonl is not None:
            fields = dict(stats_dict, timestamp=timestamp)
            stats_jsonl.write(json.dumps(fields) + "\n")
            stats_jsonl.flush()
        if stats_tfevents is not None:
            global_step = int(cur_nimg / 1e3)
            walltime = timestamp - start_time
            for name, value in stats_dict.items():
                stats_tfevents.add_scalar(
                    name, value.mean, global_step=global_step, walltime=walltime
                )
            for name, value in stats_metrics.items():
                stats_tfevents.add_scalar(
                    f"Metrics/{name}", value, global_step=global_step, walltime=walltime
                )
            stats_tfevents.flush()
        if progress_fn is not None:
            progress_fn(cur_nimg // 1000, total_kimg)

        # Update state.
        cur_tick += 1
        tick_start_nimg = cur_nimg
        tick_start_time = time.time()
        maintenance_time = tick_start_time - tick_end_time
        if done:
            break

    # Done.
    if rank == 0:
        print()
        print("Exiting...")


# ----------------------------------------------------------------------------
